from datetime import date
import math, re
import numpy as np
import pandas as pd
def _parse_year(value):
if value is None:
return None
if isinstance(value, float) and math.isnan(value):
return None
if isinstance(value, int):
return value
if isinstance(value, float):
return int(value)
s = str(value).strip()
if s == "":
return None
s_num = s.replace(",", "")
if re.fullmatch(r"[+-]?\d+(\.\d+)?", s_num):
try:
return int(float(s_num))
except Exception:
return None
m = re.search(r"(\d{4})", s)
if m:
return int(m.group(1))
return None
def aging_score(value):
year = _parse_year(value)
if year is None:
return 0
current_year = date.today().year
age = current_year - year
if age < 0 or year < 1800:
return 0
if age >= 40:
return 5
elif age >= 30:
return 4
elif age >= 20:
return 3
elif age >= 10:
return 2
elif age >= 0:
return 1
else:
return 0
def _parse_floor_count(value):
if value is None:
return None
if isinstance(value, float) and math.isnan(value):
return None
if isinstance(value, (int, float)):
n = int(float(value))
return n if n >= 0 else None
s = str(value).strip()
if s == "":
return None
s_num = s.replace(",", "")
if re.fullmatch(r"[+-]?\d+(\.\d+)?", s_num):
try:
n = int(float(s_num))
return n if n >= 0 else None
except Exception:
return None
m = re.search(r"(-?\d+)", s)
if m:
n = int(m.group(1))
return n if n >= 0 else None
return None
def aboveground_floors_score(value):
floors = _parse_floor_count(value)
if floors is None:
return 0
if floors >= 30:
return 5
elif floors >= 20:
return 4
elif floors >= 10:
return 3
elif floors >= 5:
return 2
elif floors >= 1:
return 1
else:
return 0
def _parse_basement_floor_count(value):
if value is None:
return None
if isinstance(value, float) and math.isnan(value):
return None
if isinstance(value, (int, float)):
try:
n = int(float(value))
return abs(n) if n != 0 else 0
except Exception:
return None
s = str(value).strip()
if s == "":
return None
m = re.fullmatch(r"[Bb]\s*(\d+)\s*[Ff]?", s)
if m:
return int(m.group(1))
m = re.search(r"지하\s*(\d+)", s)
if m:
return int(m.group(1))
m = re.search(r"(-?\d+)", s.replace(",", ""))
if m:
try:
return abs(int(m.group(1)))
except Exception:
return None
return None
def basement_floors_score(value):
floors = _parse_basement_floor_count(value)
if floors is None:
return 0
if floors >= 3:
return 3
elif floors >= 2:
return 2
elif floors >= 1:
return 1
else:
return 0
MAIN_USE_SCORE_MAP = {
'숙박시설': 9.0,
'야영장시설': 9.0,
'관광휴게시설': 9.0,
'공장': 8.0,
'창고시설': 8.0,
'노유자시설': 7.0,
'교육연구시설': 7.0,
'교육연구및복지시설': 7.0,
'의료시설': 7.0,
'수련시설': 7.0,
'제2종근린생활시설': 5.0,
'근린생활시설': 5.0,
'제1종근린생활시설': 5.0,
'종교시설': 5.0,
'문화및집회시설': 5.0,
'운동시설': 5.0,
'업무시설': 5.0,
'판매시설': 5.0,
'위락시설': 5.0,
'판매및영업시설': 5.0,
'기타제1종근린생활시설': 5.0,
'생활편익시설': 5.0,
'소매점': 5.0,
'동물및식물관련시설': 4.0,
'위험물저장및처리시설': 4.0,
'자원순환관련시설': 4.0,
'분뇨.쓰레기처리시설': 4.0,
'방송통신시설': 4.0,
'자동차관련시설': 4.0,
'장례시설': 4.0,
'운수시설': 4.0,
'교정및군사시설': 4.0,
'국방,군사시설': 4.0,
'발전시설': 4.0,
'묘지관련시설': 4.0,
'단독주택': 2.0,
'공동주택': 2.0,
'다가구주택': 2.0,
'공공용시설': 1.0,
}
def main_use_score_exact(value) -> float:
if value is None:
return 0.0
if isinstance(value, float) and math.isnan(value):
return 0.0
s = str(value).strip()
if s == "" or s.lower() == "nan":
return 0.0
return MAIN_USE_SCORE_MAP.get(s, 0.0)
STRUCTURE_SCORE_MAP = {
'철근콘크리트구조': 0.0,
'콘크리트구조': 0.0,
'프리케스트콘크리트구조': 0.0,
'보강콘크리트조': 0.0,
'기타콘크리트구조': 0.0,
'라멘조': 0.0,
'일반철골구조': 2.0,
'경량철골구조': 2.0,
'강파이프구조': 2.0,
'철파이프조': 2.0,
'기타강구조': 2.0,
'스틸하우스조': 2.0,
'단일형강구조': 2.0,
'철골구조': 2.0,
'공업화박판강구조(PEB)': 2.0,
'트러스구조': 2.0,
'철골콘크리트구조': 2.0,
'철골철근콘크리트구조': 2.0,
'철골철근콘크리트합성구조': 2.0,
'기타철골철근콘크리트구조': 2.0,
'일반목구조': 5.0,
'목구조': 5.0,
'통나무구조': 5.0,
'트러스목구조': 5.0,
'벽돌구조': 4.0,
'블록구조': 4.0,
'시멘트블럭조': 4.0,
'조적구조': 4.0,
'기타조적구조': 4.0,
'석구조': 4.0,
'흙벽돌조': 4.0,
'조립식판넬조': 3.0,
'컨테이너조': 3.0,
'막구조': 1.0,
'기타구조': 1.0,
}
def structure_score(value) -> float:
if value is None:
return 0.0
if isinstance(value, float) and math.isnan(value):
return 0.0
s = str(value).strip()
if not s or s.lower() == "nan":
return 0.0
if s in STRUCTURE_SCORE_MAP:
return STRUCTURE_SCORE_MAP[s]
if ('목' in s) or ('통나무' in s):
return 5.0
if ('조적' in s) or ('벽돌' in s) or ('블록' in s) or ('석' in s):
return 4.0
if ('조립' in s) or ('판넬' in s) or ('컨테이너' in s):
return 3.0
if ('철골' in s) or ('강구조' in s) or ('스틸' in s) or ('파이프' in s):
return 2.0
if ('막' in s) or ('특수' in s):
return 1.0
if ('콘크리트' in s) or ('라멘' in s):
return 0.0
return 0.0
def _parse_nonneg_int_count(value):
if value is None:
return None
if isinstance(value, float) and math.isnan(value):
return None
if isinstance(value, (int, float)):
n = int(float(value))
return n if n >= 0 else None
s = str(value).strip()
if s == "" or s.lower() == "nan":
return None
m = re.search(r"(\d+)", s.replace(",", ""))
if m:
n = int(m.group(1))
return n if n >= 0 else None
return None
def emergency_elevator_score(value) -> float:
n = _parse_nonneg_int_count(value)
if n is None:
return 0.0
if n == 0:
return 5.0
elif n == 1:
return 4.0
elif n == 2:
return 3.0
elif n == 3:
return 2.0
elif n == 4:
return 1.0
else:
return 0.0
def firestation_distance_score(dist_m, cap_over_max=True, invalid_to_nan=True):
arr = np.asarray(dist_m, dtype=float)
if invalid_to_nan:
arr = np.where(arr < 0, np.nan, arr)
default_val = 5.0 if cap_over_max else np.nan
scores = np.select(
[arr < 1000, arr < 3000, arr < 5000, arr < 7000, arr < 9000],
[1.0, 2.0, 3.0, 4.0, 5.0],
default=default_val
)
if np.isscalar(dist_m):
return float(scores.item())
if isinstance(dist_m, pd.Series):
return pd.Series(scores, index=dist_m.index, name=getattr(dist_m, "name", None))
return scores
def hydrant_distance_score(dist_m, cap_over_max=True, invalid_to_nan=True):
arr = np.asarray(dist_m, dtype=float)
if invalid_to_nan:
arr = np.where(arr < 0, np.nan, arr)
default_val = 5.0 if cap_over_max else np.nan
scores = np.select(
[arr <= 30, arr <= 60, arr <= 90, arr <= 120, arr <= 150],
[1.0, 2.0, 3.0, 4.0, 5.0],
default=default_val
)
scores = np.where(np.isnan(arr), np.nan, scores)
if np.isscalar(dist_m):
return float(np.asarray(scores).item())
if isinstance(dist_m, pd.Series):
return pd.Series(scores, index=dist_m.index, name=getattr(dist_m, "name", None))
return scores
# 점수 산정
df = pd.read_csv("./Data/건축물대장_소화전_소방서거리.csv")
df["건물노후도점수"] = df["사용승인년도"].apply(aging_score)
df["지상층수점수"] = df["지상층수"].apply(aboveground_floors_score)
df["지하층수점수"] = df["지하층수"].apply(basement_floors_score)
df["주용도점수"] = df["주용도코드명"].apply(main_use_score_exact)
df["구조점수"] = df["구조코드명"].apply(structure_score)
df["비상용승강기점수"] = df["비상용승강기수"].apply(emergency_elevator_score)
df["소방서거리점수"] = df["소방서거리"].apply(firestation_distance_score)
df["소화전거리점수"] = df["소화전거리"].apply(hydrant_distance_score)
df["종합점수"] = df["건물노후도점수"] + df["지상층수점수"] + df["지하층수점수"] + df["주용도점수"] + df["구조점수"] + df["비상용승강기점수"] + df["소방서거리점수"] + df["소화전거리점수"]
df.to_csv("./Data/건축물대장_통합_점수.csv")